# The RobotriX: An eXtremely Photorealistic and Very-Large-Scale Indoor Dataset of Sequences with Robot Trajectories and Interactions

## TL;DR

The RobotriX dataset offers an extremely photorealistic, large-scale indoor dataset with synchronized RGB-D, 3D data, and detailed annotations, enabling advanced deep learning research in robotic vision.

## Contribution

This paper introduces the RobotriX, a highly realistic, large-scale indoor dataset with synchronized multimodal data and annotations, created using Unreal Engine and human-in-the-loop data collection.

## Key findings

- 38 semantic classes with 8 million frames
- High-quality RGB-D and 3D annotations at 60 fps
- Enables large-scale deep learning for robotic vision tasks

## Abstract

Enter the RobotriX, an extremely photorealistic indoor dataset designed to enable the application of deep learning techniques to a wide variety of robotic vision problems. The RobotriX consists of hyperrealistic indoor scenes which are explored by robot agents which also interact with objects in a visually realistic manner in that simulated world. Photorealistic scenes and robots are rendered by Unreal Engine into a virtual reality headset which captures gaze so that a human operator can move the robot and use controllers for the robotic hands; scene information is dumped on a per-frame basis so that it can be reproduced offline to generate raw data and ground truth labels. By taking this approach, we were able to generate a dataset of 38 semantic classes totaling 8M stills recorded at +60 frames per second with full HD resolution. For each frame, RGB-D and 3D information is provided with full annotations in both spaces. Thanks to the high quality and quantity of both raw information and annotations, the RobotriX will serve as a new milestone for investigating 2D and 3D robotic vision tasks with large-scale data-driven techniques.